The invention relates to a method for training neural networks, to a method for prognosis by means of neural networks and to a system for determining a prognosis value and its error.
A large number of possible applications for neural networks are known from the prior art. Neural networks are used for data-driven modelling, for example for physical, biological, chemical and technical processes and systems, cf. Babel W.: Possible uses of neural networks in industry: pattern recognition with the aid of supervised learning methods—with examples from traffic and medical technology, Expert Verlag, Renningen-Malmsheim, 1997. In particular, the fields in which neural networks can be used include process optimization, image processing, pattern recognition, robot control and medical technology.
Before a neural network can be used for the purposes of prognosis or optimization, it has to be trained. The weightings of the neurons are in this case usually adjusted by an iterative method with the aid of training data, cf. Bärmann F.: Process modelling: modelling of continuous systems with neural networks, the NN-tool Internet site, www.baermann.de and Bärmann F.: Neural networks. Lecture text. Technical College of Gelsenkirchen, School of Physical Technology, Department of Neuro-Information Technology, 1998.
DE 195 31 967 discloses a method for training a neural network with the nondeterministic behaviour of a technical system. The neural network is in this case incorporated into a control loop so that, as it is output quantity, the neural network outputs a manipulated variable to the technical system and the technical system generates, from the manipulated variable delivered by the neural network, controlled variable which is delivered to the neural network as an input quantity. Noise with a known noise distribution is superimposed on the controlled variable before it is delivered to the technical system.
Other methods for training neural networks are known from DE 692 28 412 T2 and DE 198 38 654 C1.
A method for estimating the prognosis error is known from EP 0 762 245 B1. Here, a plurality of neural networks having different training parameters (e.g. different initialisation) are trained with the original data. The prognosis error is obtained by comparing the discrepancies of the prognosed quantities. A disadvantage of this method is that the estimation of the prognosis error is not influenced by information about the measurement accuracy of the measurement data used for the training.
A method for estimating the reliability of a prognosis output by a neural network is furthermore known from the prior art: Protzel P., Kindermann L., Tagscherer M., Lewandowski A.: “Estimation of the reliability of neural network prognoses in process optimization”, VDI Report No. 1526, 2000.